Handbook of psychology volume 7 educational psychology
Memory and Information Processes
Download 9.82 Mb. Pdf ko'rish
|
- Bu sahifa navigatsiya:
- Cognitive System: Architecture of the Cognitive System 51
- Information Processing in Solving a Mathematics Problem 54
- TWO VIEWS OF INFORMATION PROCESSING THEORY
- Two Views of Information Processing Theory 49
- MAJOR CONTRIBUTIONS OF INFORMATION PROCESSING THEORY
- Cognitive Processes: Cognitive Task Analysis
- Mental Representations: Types of Knowledge
- Major Contributions of Information Processing Theory 51 Figure 3.1
Memory and Information Processes RICHARD E. MAYER 47 AN INFORMATION PROCESSING VIEW OF LEARNING AND COGNITION 47 HISTORICAL OVERVIEW 47 Associationist View 47 Gestalt View 48 TWO VIEWS OF INFORMATION PROCESSING THEORY 48
MAJOR CONTRIBUTIONS OF INFORMATION PROCESSING THEORY 50
50 Mental Representations: Types of Knowledge 50 Cognitive System: Architecture of the Cognitive System 51 INFORMATION PROCESSING AND INSTRUCTION 53
CONCLUSION 55 REFERENCES 56 AN INFORMATION PROCESSING VIEW OF LEARNING AND COGNITION How does the human mind work? What happens when some- one learns or when someone solves a problem? According to the information processing view, the human mind works by forming mental representations and applying cognitive processes to them. This definition has two elements: (a) The content of cognition is mental representations, and (b) the ac- tivity of cognition involves cognitive processes. In learning, the learner takes incoming information received through the eyes or ears and applies a series of cognitive processes to the incoming information, resulting in the construction of a se- ries of mental representations. For example, as you read the words in this paragraph you form a series of mental represen- tations by applying appropriate cognitive processes such as mentally selecting important ideas, mentally organizing them into a coherent cognitive structure, and mentally relating them with prior knowledge. In this chapter I provide a brief historical overview of the precursors to the information pro- cessing view of learning and cognition, describe two versions of the information processing view, examine three major contributions of the information processing view, and then exemplify how it contributes to theories of learning and cognition.
For more than 100 years psychologists have conducted re- search aimed at understanding how knowledge is represented and processed in human minds. Such issues fell under the domain of science as psychology entered the twentieth cen- tury, heralded by the publication of Ebbinghaus’s pioneering memory studies in 1885 (Ebbinghaus, 1964) and Thorndike’s pioneering learning studies in 1898 (Thorndike, 1965). Dur- ing the first half of the twentieth century two competing views of learning emerged—the associationist view of learn- ing as strengthening of associations and the Gestalt view of learning as building cognitive structures. Associationist View According to the associationist view, the content of cognition consists of nodes and associations between them and the process of cognition consists of the strengthening and weak- ening of associations. For example, in Thorndike’s (1965) classic study of animal learning, a hungry cat was placed in a wooden box. The cat could escape by pulling a hanging loop of string that opened a door allowing the cat to get out and eat some nearby food. Thorndike noted that on the first day, the cat engaged in many extraneous behaviors before accidentally 48 Memory and Information Processes pulling the string, but on successive days the number of extra- neous behaviors decreased. After many days, the cat pulled the loop of string shortly after being placed in the box. According to Thorndike, the cat began with a habit family hierarchy—an ordered set of responses associated with being placed in an enclosed box. The cat would try the most strongly associated response first (e.g., thrusting its paw through the slats of the box), and when it failed, the strength of the associ- ation to that response would be weakened. Eventually, the cat would pull the loop of string and get out, thus increasing the association to that response. Over many days, the extraneous responses became very weakly associated with being in the box, and pulling the string became very strongly associated with being in the box. Thus, Thorndike offered a clear vision of learning as the strengthening and weakening of stimulus- response (S-R) associations and memory as the processing of linked nodes in a network—a vision that dominated psychol- ogy through the 1950s and still flourishes today in revised form.
Gestalt View According to the Gestalt view, the content of cognition con- sists of coherent structures, and the process of cognition con- sists of building them. For example, Kohler (1925) placed an ape in a pen with crates on the ground and a bunch of bananas hanging overhead out of reach. Kohler observed that the ape looked around and then suddenly placed the crates on top of one another to form a ladder leading to the bananas, allowing the ape to climb the stairs and grasp the bananas. According to Kohler, the ape learned by insight—mentally re- organizing the objects in the situation so they fit together in a way that accomplished the goal. Thus, insight is a process of structure building (Mayer, 1995). The Gestalt approach rose to prominence in the 1930s and 1940s but is rarely mentioned today. Nonetheless, the Gestalt theme of cognition as structure building underlies core topics in cognitive science including the idea of schemas, analogical reasoning, and meaningful learning. By the 1950s and 1960s, the associationist and Gestalt views were reshaped into a new view of cognition, called information processing (Lachman, Lachman, & Butterfield, 1979). The information processing view eventually became the centerpiece of cognitive science—the interdisciplinary study of cognition. A core premise in cognitive science is that cognition involves computation; that is, cognition occurs when you begin with a representation as input, apply a process, and create a representation as output. For example, in a review of the field of cognitive science, Johnson-Laird (1988, p. 9) noted, “Cognitive science, sometime explicitly and sometimes implicitly, tries to elucidate the workings of the mind by treat- ing them as computations.” Human cognition on any task can be described as a series of cognitive processes (i.e., a descrip- tion of the computations that were carried out) or as a series of transformations of mental representations (i.e., a description of the inputs and outputs for each computation).
A central problem of the information processing approach is to clarify the nature of mental representations and the nature of cognitive processes. This task is made more difficult by the fact that researchers cannot directly observe the mental rep- resentations and cognitive processes of other people. Rather, researchers must devise methods that allow them to infer the mental representations and cognitive processes of others based on their behavior (including physiological responses). In the evolution of the information processing approach to learning and memory, there have been two contrasting ver- sions: the classical and constructivist view (Mayer, 1992a, 1996a). Leary (1990) showed how progress in psychological theo- ries can be described as a progression of metaphors, and Mayer (1992a, 2001) described several major metaphors of learning and memory that have emerged during the last cen- tury, including viewing knowledge as information versus viewing knowledge as cognitive structure. A major challenge of the information processing view—and the field of cogni- tive science that it serves—is to clarify the status of the knowledge as information metaphor (which is part of the clas- sical view) and the knowledge as cognitive structure metaphor (which is part of the constructivist view). Classical View The classic view is based on a human-machine metaphor in which the human mind is like a computer; knowledge is rep- resented as data that can be processed by a computer, and cognition is represented as a program that specifies how data are processed. According to the classical view, humans are processors of information. Information is a commodity that can be transferred from one mind to another as a series of symbols. Processing involves applying an algorithm to infor- mation such that a series of symbols is manipulated accord- ing to a step-by-step procedure. For example, when given a problem such as “x ϩ 2 ϭ 4, solve for x,” a learner forms a mental representation of the problem such as “x ϩ 2 ϭ 4” and applies operators such as mentally subtracting 2 to both Two Views of Information Processing Theory 49 sides in order to generate a new mental representation, namely “x ϭ 2.”
The classical information processing approach developed in the 1950s, 1960s, and 1970s, although its roots predate psychology (Lachman et al., 1979). For example, more than 250 years ago De La Mettrie (1748/1912) explored the idea that the human mind works like a complex machine, and the classical information processing view can be seen in Atkinson and Shiffrin’s (1968) theory of the human memory system and Newell and Simon’s (1972) theory of human problem solving. For example, Newell and Simon (1972) developed a computer simulation designed to solve a variety of prob- lems ranging from chess to logic to cryptarithmetic. In the problem-solving program, information consists of “symbol structures” (p. 23) such as a list, tree, or network, and pro- cessing consists of “executing sequences of elementary infor- mation process” (p. 30) on symbol structures. A problem is represented as a problem space consisting of the initial state, the goal state, and all possible intervening states with links among them. The process of searching the space is accom- plished by a problem-solving strategy called means-ends analysis, in which the problem solver sets a goal and carries it out if possible or determines an obstacle that must be over- come if it is not (see Mayer, 1992b). Thus, problem solving involves applying processes to a symbolic representation of a problem: If the application is successful, the representation is changed; if it is not successful, a new process is selected based on a means-ends analysis strategy. In a complex prob- lem, a long series of information processes may be applied, and many successive representations of the problem state may be created. Two limitations of the classical view—humans as infor- mation processors—concern the characterization of informa- tion as an objective commodity and the characterization of processing as the application of algorithms. Although such characterizations may mesh well with highly contrived labo- ratory tasks, they appear too limited to account for the full range of human learning in complex real-world situations. For example, Metcalfe (1986a, 1986b; Metcalfe & Wiebe, 1987) showed that people use different cognitive processing for insight problems (requiring a major reorganization of the problem) and noninsight problems (requiring the step-by-step application of a series of cognitive processes). For insight problems people are not able to predict how close they are to solving the problem (inconsistent with the step-by-step think- ing posited by the classical view), but for noninsight prob- lems they are able to gage how close they are to solution (consistent with the step-by-step thinking posited by the classical view). Apparently, the classical view may offer a reasonable account of how people think about noninsight problems but not how they think about insight problems. Constructivist View The constructivist view is based on the knowledge con- struction metaphor, in which the human mind is a sort of con- struction zone in which learners actively create their own knowledge based on integrating what is presented and what they already know. According to the constructivist view, learn- ers are sense makers who construct knowledge. Knowledge is a mental representation that exists in a human mind. Unlike in- formation, which is an objective entity that can be moved from one mind to another, knowledge is a personal construction that cannot be moved directly from one mind to another. Construc- tion involves cognitive processing aimed at sense making, including attending to relevant portions of the presented mate- rial, mentally organizing the material into a coherent structure, and mentally integrating the material with relevant existing knowledge. Unlike the view of cognitive processing as apply- ing algorithms, cognitive processing involves orchestrating cognitive strategies aimed at sense making. For example, as you read this section, you may mentally select relevant ideas such as the classical view of information and processing and the constructivist view of knowledge and construction; you may organize them into a matrix with classical and construc- tivist as rows and nature of information and nature of process- ing as columns; and you may integrate this material with your previous knowledge about these topics. The constructivist approach developed in the 1980s and 1990s, although its earlier proponents include Bartlett’s (1932) theory of how people remember stories and Piaget’s (1971) theory of how children learn. For example, Bartlett argued that when learners are presented with a folk story, they assimilate story elements to their existing schemas and men- tally reorganize the story in a way that makes sense to them. Similarly, Piaget showed how children assimilate their experi- ences with their existing schemas in an attempt to make sense of their environment. More recently, the constructivist view can be seen in Ausubel’s (1968) theory of assimilative learning and Wittrock’s (1990) theory of generative learning. In both theories, learning involves connecting what is presented with what the learner already knows, so the outcome of learning de- pends both on the material presented by the instructor and the schemas used by the learner. Although the constructivist view addresses some of the limitations of the classical view, major limitations of the con- structivist view include the need to account for the social and cultural context of cognition and the need to account for the biological and affective bases of cognition. In particular, 50 Memory and Information Processes the constructivist view focuses on cognitive changes within individual learners, but this view can be expanded by consid- ering how the learner’s cognitive processing is mediated by the learner’s surrounding social and cultural environment. The constructivist view focuses on what can be called cold cognition (i.e., cognitive processing in isolation), but this view can be expanded by also considering the role of the learner’s emotional and motivational state.
Three important contributions of the information process- ing approach are techniques for analyzing cognitive process- ing (e.g., “What are the cognitive processes involved in carrying out a cognitive task?”), techniques for analyzing men- tal representations (e.g., “How is knowledge represented in memory?”), and a general description of the architecture of the human cognitive system (e.g., “How does information flow through the human memory system?”).
A fundamental contribution of information processing theory is cognitive task analysis—techniques for describing the cog- nitive processes that a person must carry out to accomplish a cognitive task. For example, consider the analogy problem dog : bark :: cat : ____, which can be read as “dog is to bark as cat is to what?” and in which the a-term is “dog,” the b-term is “bark,” the c-term is “cat,” and the d-term is un- known. What are the cognitive processes that a problem solver must go through to solve this problem? Based on a cognitive task analysis, solving an analogy problem can be broken down into five basic steps (Mayer, 1987; Sternberg, 1977):
sentation of the words and accompanying punctuation, 2. Inferring—that is, determining the relation between the a-term and the b-term (e.g., the b-term is the sound that the a-term makes),
it corresponds to the a-term (e.g., the a-term is a kind of animal that makes sounds, and the c-term is another kind of animal that makes sounds), 4. Applying—that is, generating a d-term based on applying the relational rule to the c-term (e.g., the sound that the c-term makes is _____), and
as writing “meow” or circling the correct answer (“meow”) on a list. Cognitive task analysis has useful educational applications because it suggests specific cognitive processes that students need to learn. For example, the cognitive task analysis of analogy problems suggests that students would benefit from instruction in how to infer the relation between the a-term and the b-term (Sternberg, 1977). To test this idea, Sternberg and Ketron (1982) taught col- lege students how to solve analogy problems by showing them how to infer the change from the a-term to the b-term and how to apply that change to the c-term. On a subsequent test of ana- logical reasoning involving new problems, trained students solved the problems twice as fast and committed half as many errors as did students who had not received training. Cognitive task analysis also offers advantages in evaluat- ing student learning outcomes. For example, instead of mea- suring the percentage correct on a test, it is possible to specify more precisely the knowledge that a student possesses— including incomplete or incorrect components. For example, suppose a student gives the following answers on an arith- metic test: 234
678 456
545 Ϫ156
Ϫ434 Ϫ327
Ϫ295 122
244 131
350 A traditional evaluation would reveal that the student cor- rectly solved 25% of the problems. However, a cognitive task analysis reveals that the student seems to be consistently ap- plying a subtraction procedure that has one incorrect step, or bug—namely, subtracting the smaller number from the larger number in each column (Brown & Burton, 1978). In specify- ing the procedure that the student is using, it becomes clear that instruction is needed to help the student replace this
According to the information processing approach, knowl- edge is at the center of cognition: Learning is the construction of knowledge; memory is the storage of knowledge; and thinking is the logical manipulation of knowledge. Therefore, information processing theorists have analyzed the types of knowledge (or mental representations): factual, conceptual, procedural, and metacognitive (Anderson et al., 2001). Fac- tual knowledge consists of facts—that is, simple descriptions of an object or element (e.g., “apples are red”). Conceptual Major Contributions of Information Processing Theory 51 Figure 3.1 An information processing model of how the human mind works. knowledge involves relations among elements within a co- herent structure that enables them to function together, and includes classification hierarchies, cause-and-effect models, explanatory principles, and organizing generalizations (e.g., the model presented in Figure 3.1). Procedural knowledge in- volves a procedure, method, or algorithm—that is, a step-by- step specification of how to do something (e.g., the procedure for how to carry out long division). Metacognitive knowl- edge involves strategies for how to coordinate one’s cogni- tive processing (e.g., knowing how to monitor the quality of one’s essay-writing activity). As you can see, factual and conceptual knowledge are knowledge of “what” (i.e., data structures), whereas procedural and metacognitive knowl- edge are knowledge of “how to” (i.e., processes for manipu- lating data structures). Knowledge is a mental representation: It is mental because it exists only in human minds; it is a representation because it is intended to denote or signify something. Representations can be classified based on the coding system used to represent them in the cognitive system such as motoric (e.g., bodily movement images), pictorial (e.g., mental images), verbal (e.g., words), or symbolic (e.g., some higher level coding sys- tem). Representations can be classified based on the input modality including haptic/kinesthetic/vestibular (e.g., bodily sensations), visual (e.g., imagery sensations), or auditory (e.g., acoustic sensations). Download 9.82 Mb. Do'stlaringiz bilan baham: |
ma'muriyatiga murojaat qiling